{"id":21130,"date":"2026-04-12T11:28:36","date_gmt":"2026-04-12T09:28:36","guid":{"rendered":"https:\/\/thesmartcityjournal.cibeles.net\/sin-categorizar\/inteligencia-artificial-y-drones-para-seleccionar-el-trigo-mas-resiliente\/"},"modified":"2026-04-12T11:31:42","modified_gmt":"2026-04-12T09:31:42","slug":"inteligencia-artificial-y-drones-para-seleccionar-el-trigo-mas-resiliente","status":"publish","type":"post","link":"https:\/\/www.thesmartcityjournal.com\/es\/agri-tech\/inteligencia-artificial-y-drones-para-seleccionar-el-trigo-mas-resiliente","title":{"rendered":"Inteligencia artificial y drones para seleccionar el trigo m\u00e1s resiliente"},"content":{"rendered":"

Una investigaci\u00f3n liderada por un equipo de la Universidad de Barcelona y del centro de investigaci\u00f3n Agrotecnio ha descubierto una forma innovadora : combinar tecnolog\u00eda avanzada e inteligencia artificial para seleccionar las mejores variedades de esta gram\u00ednea<\/span><\/i><\/p>\n

El trabajo, publicado en la revista Plant Phenomics, propone un cambio de mirada: es necesario fijarse no solo en el rendimiento, sino tambi\u00e9n en la capacidad del trigo de mantener cosechas consistentes aunque el clima sea cambiante. Las conclusiones apuntan a que esta combinaci\u00f3n de productividad y estabilidad es clave para asegurar cosechas seguras en condiciones ambientales cambiantes.<\/span><\/p>\n

Los autores del estudio son los investigadores Jara Jauregui Bes\u00f3, Jos\u00e9 Luis Araus y Shawn Carlisle Kefauver, del Departamento de Biolog\u00eda Evolutiva, Ecolog\u00eda y Ciencias Ambientales de la Facultad de Biolog\u00eda de la UB y Agrotecnio; Nieves Aparicio y Sara \u00c1lvarez, del Instituto Tecnol\u00f3gico Agrario de Castilla y Le\u00f3n (ITACyL), y Mar\u00eda Teresa Nieto Taladriz, del Instituto Nacional de Investigaci\u00f3n y Tecnolog\u00eda Agraria y Alimentaria (INIA-CSIC).<\/span><\/p>\n

Drones para estudiar los cultivos de trigo<\/h2>\n

El equipo ha analizado 64 variedades de trigo duro cultivadas en dos entornos mediterr\u00e1neos distintos: en regad\u00edo y en secano. El objetivo era identificar qu\u00e9 genotipos combinan una elevada producci\u00f3n con una respuesta estable en ambientes variables, con diferencias en temperatura y disponibilidad de agua.<\/span><\/p>\n

Uno de los resultados m\u00e1s asombrosos es que las variedades seleccionadas no son las que mantienen las hojas verdes m\u00e1s tiempo hasta el final de la temporada, sino las que crecen con fuerza al principio y maduran un poco antes.<\/span><\/p>\n

Por el contrario, las l\u00edneas descartadas presentaban un vigor inicial bajo y manten\u00edan las hojas verdes durante m\u00e1s tiempo, lo que no garantiza un mejor rendimiento.<\/span><\/p>\n

En el marco del trabajo, el equipo utiliz\u00f3 sensores terrestres y drones equipados con c\u00e1maras RGB, multiespectrales y t\u00e9rmicas, que permitieron monitorear el desarrollo de los cultivos durante todo el ciclo. Esta tecnolog\u00eda permite obtener informaci\u00f3n clave del trigo antes de segar, eliminando la necesidad de hacerlo y reduciendo tanto los costes como el tiempo necesario para su estudio.<\/span><\/p>\n

Con todos estos datos, el equipo entren\u00f3 modelos de inteligencia artificial capaces de predecir con gran precisi\u00f3n tanto el rendimiento como la estabilidad productiva de las variedades.<\/span><\/p>\n

Esta estrategia puede convertirse en una herramienta muy \u00fatil para los programas de mejora gen\u00e9tica y puede ayudar a desarrollar variedades de trigo preparadas para afrontar los retos del cambio clim\u00e1tico.<\/span><\/p>\n

M\u00e1s verde no siempre significa mejor<\/h2>\n

Los investigadores analizaron, en primer lugar y por separado, los rasgos de rendimiento y estabilidad del trigo duro. Vieron que los genotipos con mayor rendimiento se caracterizan por un alto vigor inicial y un verdor sostenido durante las fases de crecimiento r\u00e1pido hasta el final del ciclo. En cambio, los genotipos m\u00e1s estables presentan un menor vigor inicial, un crecimiento m\u00e1s lento y un ciclo m\u00e1s corto, lo que les permite aprovechar mejor los recursos disponibles para el grano. Con el objetivo de identificar un equilibrio entre estos mecanismos compensatorios, los expertos desarrollaron un m\u00e9todo de selecci\u00f3n de variedades que combina un rendimiento competitivo con una buena estabilidad.<\/span><\/p>\n

El estudio concluye que un establecimiento vigoroso inicial combinado con la maduraci\u00f3n anticipada parece clave para conseguir cosechas m\u00e1s constantes en condiciones ambientales variables, lo que ayudar\u00eda a que el trigo pudiera afrontar mejor la sequ\u00eda y las temperaturas elevadas.<\/span><\/p>\n

\u00a0Art\u00edculo de referencia:<\/span><\/i><\/p>\n

Jauregui-Bes\u00f3, Jara; Aparicio, Nieves; \u00c1lvarez, Sara; Nieto-Taladriz, Mar\u00eda Teresa; Araus, Jos\u00e9 Luis; Carlisle Kefauver, Shawn. \u00abMulti-sensor phenotyping of yield and yield stability for genotype selection in durum wheat\u00bb. Plant Phenomics, febrero de 2026. DOI:10.1016\/j.plaphe.2026.100178.<\/span><\/i><\/p>\n

Foto: Jara Jauregui Bes\u00f3<\/span><\/i><\/p>\n

Foto Jara Ja\u00faregui-Beso<\/span><\/i>\u00a0<\/span><\/i><\/p>\n","protected":false},"excerpt":{"rendered":"

Una investigaci\u00f3n liderada por un equipo de la Universidad de Barcelona y del centro de investigaci\u00f3n Agrotecnio ha descubierto una forma innovadora : combinar tecnolog\u00eda avanzada e inteligencia artificial para seleccionar las mejores variedades de esta gram\u00ednea El trabajo, publicado en la revista Plant Phenomics, propone un cambio de mirada: es necesario fijarse no solo\u2026<\/p>\n","protected":false},"author":2,"featured_media":21129,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_scj_primary_category_id":75,"_scj_featured":false,"_scj_featured_from":"","_scj_featured_until":"","_scj_featured_order":0,"_scj_visibility_class":"current","_scj_layout_family":"standard","_scj_article_style":"","_scj_display_overrides":[],"_scj_intro_image_id":21129,"_scj_intro_alt":"Inteligencia artificial y drones para seleccionar el trigo m\u00e1s 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